Systems and methods for measuring efficiencies of hvacr systems

ABSTRACT

Computer-implemented methods and related systems enable measurement of efficiency of Heating, Ventilation, Air Conditioning and Refrigeration (HVACR) systems. The method includes receiving data regarding an HVACR system at a processor. The data is received from one or more probes installed on the HVACR system. The method also includes processing the data to determine an efficiency of the HVACR system and displaying the measured efficiency on a network device. Accordingly, technicians, engineers and property managers can monitor HVACR systems and can use the measured data to maintain or repair HVACR systems, to understand their effectiveness and cost-efficiencies and to design HVACR systems for building spaces.

BACKGROUND

According to some estimates, approximately half of the energy consumed in an average residential household comes from heating, ventilation, air conditioning, and refrigeration (HVACR) systems. The average household HVACR lifespan is 12 to 15 years, when properly installed and maintained. A typical residential air conditioning system that is seldom maintained can lose approximately 26% of its efficiency after 10 years and approximately 37% of its efficiency after 15 years. A residential air conditioning system that is maintained properly still loses approximately 10% of its efficiency after 10 years and approximately 14% of its efficiency after 15 years.

To put these figures in perspective, if an air conditioning system is 15 years old and hasn't been maintained properly, approximately 18% of a power bill that includes this air conditioning system is probably due to an inefficient of the system. This 18% does not even consider other factors that can decrease the efficiency of the air conditioning system, such as an inadequate home insulation factor caused by air imbalance, poor insulation, improperly set fan speeds and air infiltration.

Accurately measuring the efficiency of an HVACR system is critical to ensure that the HVACR system is running properly, and money is not being wasted. However, current methods for measuring the efficiency of HVACR systems can be inaccurate and lead to extreme misdiagnosis. For example, measuring refrigerant properties in an air conditioning system may be an unreliable way to determine efficiency of an air conditioning system because other factors, such as refrigerant impurities and or degradation, can change the heat transfer properties dramatically. Thus, improvements can be made to improve the accuracy of efficiency measurements of HVACR systems.

The subject matter claimed herein is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one example technology area where some embodiments described herein may be practiced.

SUMMARY

In some embodiments, a computer-implemented method for measuring the efficiency of an HVACR system is disclosed. The method may include receiving data regarding an HVACR system at a processor, wherein the data is received from one or more probes installed on the HVACR system. The method may also include processing the data to determine an efficiency of the HVACR system. The method may further include displaying the efficiency data on a network device.

In some embodiments, the probe may be an air probe and the received data may include at least one of a temperature, a relative humidity, a barometric pressure, and an air flow rate.

In some embodiments, the probe may be a fluid probe and the received data may include a temperature.

It is to be understood that both the foregoing summary and the following detailed description are explanatory and are not restrictive of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1 illustrates an example system configured for measuring the efficiency of an HVACR system;

FIG. 2 illustrates exemplary probe tools;

FIG. 3 illustrates an exemplary monitoring system;

FIGS. 4-34 are exemplary pages from a probe app; and

FIG. 35 illustrates an example computer system that may be employed in a system for measuring the efficiency of an HVACR system.

DETAILED DESCRIPTION

Current methods for measuring the efficiency of HVACR systems can be inaccurate and lead to extreme misdiagnosis. Accurately measuring the efficiency of an HVACR system can be critical to a variety of different people and entities. For example, with accurate efficiency measurements: (a) power companies can add more people to the same grid; (b) residential consumers and complexes, and commercial businesses can save money on their utility bills and extend the life of their system; (c) industrial entities can react fast to problems, lower their overhead of maintenance personnel, extend the life of their equipment and lower their utility expenses; and (d) HVACR technicians can increase their value by quickly identifying problems.

Some embodiments disclosed herein may include air and fluid probes that can be used to accurately measure the efficiency of HVACR systems. These probes can have a variety of uses in residential settings. For example, in residential settings, probes can:

-   -   provide a notification when an HVACR system is not running         efficiently and/or when air filters need to be replaced;     -   provide efficiency updates on an HVACR system in real-time;     -   give real-time system efficiency updates;     -   monitor how residents are using an HVACR system;     -   extend the life of the HVACR system; (f) provide information in         real-time on how much the inefficiency of an HVACR system is         costing in its current state and a forecast of the system's         future inefficiency so that the consumer can prioritize         financial decisions relating to the HVACR system;     -   help to identify problems with an HVACR system more quickly and         more accurately; and     -   pull historic and real-time weather data to ensure accurate         space r-value readings.

In addition to uses in residential settings, these probes have a variety of uses in commercial settings. To begin, these probes can be installed on many different commercial systems. For example, these probes may be installed on split systems, package units, chill water systems, geothermal systems, water source heat pumps, walk-in coolers, and walk-in freezers to name a few. In addition, probes used in a commercial setting can

-   -   save money on electric bills and reduce operating expenses;     -   integrate data from multiple probes that may be viewed together         on a single easy to navigate platform;     -   provide a notification when an HVACR system is not running         efficiently and/or when air filters need to be replaced;     -   detect and measure volatile organic compounds (VOCs), carbon         dioxide, and other gasses (air probes);     -   provide real-time system efficiency updates;     -   analyze evaporator and condenser barrels in real-time to         identify when scaling, refrigerant charge or water flow are         becoming an issue;     -   analyze cooling towers in real-time to see when scaling, fan         speed, water flow or humidity are an issue; and     -   consider historic and real-time weather data to make sure HVACR         systems are running at maximum efficiency in any environment.

Turning to the figures, FIG. 1 illustrates an example system 100 configured for measuring the efficiency of an HVACR system. The system 100 may include a network 102, probes 104, an HVACR system 105, a webserver 106, a user device 108, and a mobile application marketplace server 110.

In some embodiments, the network 102 may be configured to communicatively couple probes 104, webserver 106, user device 108, and mobile application marketplace server 110 to one another and to other network devices using one or more network protocols, such as the network protocols available in connection with the World Wide Web. In some embodiments, the network 102 may be any wired or wireless network, or combination of multiple networks, configured to send and receive communications (e.g., via data packets) between systems and devices. In some embodiments, the network 102 may include a Personal Area Network (PAN), a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Storage Area Network (SAN), a cellular network, the Internet, or some combination thereof.

HVACR system 105 may include any heating, ventilation, air-conditioning, and/or refrigeration system. HVACR system 105 may include one or more conduits, channels, ducts, tubes, and pipes through which gasses or liquids may flow. HVACR system 105 may include one or more evaporator coils, evaporator barrels, condenser barrels, cooling towers, and/or boilers. HVACR system 105 may be configured for a residential use and/or commercial use.

In some embodiments, probes 104 may be devices for measuring and transmitting data from within HVACR system 105. Probes 104 may include air probes 104 a and fluid probes 104 b. Air probes 104 a may be configured to measure and transmit data relating to a gas within HVACR system 105. For example, air probes 104 a may include one or more sensors configured to measure a temperature, relative humidity, barometric pressure, and/or air flow rate of a gas within HVACR system 105. Fluid probes 104 b may be configured to measure and transmit data relating to a liquid within HVACR system 105. For example, fluid probes 104 b may include one or more sensors configured to measure a temperature, density, and/or flow rate of a liquid within HVACR system 105.

Probes 104 may transmit any measured data through network 102 to any network device connected to network 102, including webserver 106 and user device 108. In addition, probes 104 may transmit measured data directly to user device 108. For example, probes 104 may transmit data to user device 108 through a wired connection or a wireless connection, such as Bluetooth.

In some embodiments, webserver 106 may be any computer system capable of communicating over the network 102 with probes 104, user device 108, and/or mobile application marketplace server 110. Webserver 106 may make one or more mobile and/or web applications available to user device 108, including probe app 112. Webserver 106 may also host one or more websites, such as web portals that include web pages, addressable at a particular web domain. Webserver 106 may further include a processor 114.

In some embodiments, probe app 112 may be a mobile app that is available for download through a server, such as mobile application marketplace server 110. Mobile application marketplace server 110 may include, for example, the APP STORE provide by Apple Computer and the ANDROID application website. Probe app 112 may alternatively be a web application that is hosted by webserver 106. In addition, probe app 112 may be a desktop app, which is downloaded and installed on a desktop computer.

In some embodiments, user device 108 is a mobile communication device such as a mobile phone or a tablet computer. In alternative embodiments, user device 108 may be a laptop computer or a desktop computer. In embodiments where user device 108 is a mobile device, probe app 112 may be a mobile app downloaded from mobile application marketplace server 110 such as the APP STORE provide by Apple Computer or the ANDROID application website. In embodiments where user device 108 is a laptop or desktop computer, probe app 112 may be provided through a web application that is hosted by webserver 106.

Modifications, additions, or omissions may be made to the system 100 without departing from the scope of the present disclosure. For example, in some embodiments, system 100 may include additional components similar to the components illustrated in FIG. 1 that each may be configured similarly to the components illustrated in FIG. 1.

Probe app 112 may be configured to receive data that is measured by probes 104 and, using this data alone or together with additional data, calculate any number of different performance levels of an HVACR system, such as enthalpy. For example, probes 104 may measure the return and supply temperatures and relative humidity levels across an evaporator coil. Using these values, probe app 112 may calculate enthalpy. Probes 104 may also measure the static pressure across the indoor unit/components to be used for further calculations and/or as a method to read when the indoor blower motor is on/off. Probe app 112 may use one or more algorithms to calculate the effective heat transfer efficiency of HVACR system 105. For example, algorithms may be used to first calculate the enthalpy removal across the evaporator coil.

p_ws_out=exp ((T_c_out/T_out)*((C_1_out*v_out)+(C_2_out*(pow (v_out,1.5)))+(C_3_out*(pow (v_out, 3)))+(C_4_out*(pow (v_out,3.5)))+(C_5_out*(pow (v_out,4)))+(C_6_out*(pow (v_out,7.5)))))*P_c_out p_w_out=(p_ws_out)*Float(cooling_Tower_ODRH_Adjust/100) Abs_Hum_Out=2.16679*p_w_out/T_out Abs_Hum_In=2.16679*p_w_r/t_r Grains_out = (Abs_Hum_Out*190.965264437)/Float(((altitude_Corr_Factor)*0.075)) Grains_in = (Abs_Hum_In*190.9652 64437)/Float(((altitude_Corr_Factor)*0.075)) Grain_Difference = Grains_out−Grains_in calc_BTU_Ton = Float(bTU_Ton_Adjust + 12000) if (return_F_Calibrated > supply_F_Calibrated) { eff_calc_BTU = calc_BTU_Ton * (cooling_Capacity/100)} else{ eff_calc_BTU = calc_BTU_Ton * (heating_Capacity/100)} if (efficiency_Pref == 0) {coP = 0} else if (efficiency_Pref == 1) {coP = rated_Efficiency} else if (efficiency_Pref == 2) {coP = rated_Efficiency} else if (efficiency_Pref == 3) {coP = rated_Efficiency * 0.293} else if (efficiency_Pref == 4) {coP = rated_Efficiency * 0.293} else {coP = rated_Efficiency * 0.293} if (efficiency_Pref_Heat == 0) {coP_Heat = 0} else if (efficiency_Pref_Heat == 1) {coP_Heat = rated_Efficiency_Heat} else if (efficiency_Pref_Heat == 2) {coP_Heat = rated_Efficiency_Heat} else if (efficiency_Pref_Heat == 3) {coP_Heat = rated_Efficiency_Heat * 0.293} else if (efficiency_Pref_Heat == 4) {coP_Heat = rated_Efficiency_Heat * 0.293} else {coP_Heat = rated_Efficiency_Heat * 0.293} MoneySaverCalculations.getInstance( ),coP_Heat = Double(coP_Heat) MoneySaverCalculations.getInstance( ).coP = Double(coP) //Enthalpy Actual Calc.'s p_ws_r = exp ((t_c_r/t_r)*((c_1_r*v_r)+(c_2_r*(pow(v_r,1.5)))+(c_3_r*(pow (v_r,3)))+(c_4_r*(pow (v_r,3.5)))+(c_5_r*(pow (v_r,4)))+(c_6_r*(pow (v_r,7.5)))))*p_c_r p_ws_s = exp ((t_c_s/t_s)*((c_1_s*v_s)+(c_2_s*(pow (v_s,1.5)))+(c_3_s*(pow (v_s,3)))+(c_4_s*(pow (v_s,3.5)))+(c_5_s*(pow (v_s,4)))+(c_6_s*(pow (v_s,7.5)))))*p_c_s p_w_r = (p_ws_r) * Float(return_RH_Calibrated)/100 p_w_s = (p_ws_s) * Float(supply_RH_Calibrated)/100 dewc_r = 240.7263/((7.591386)/(log10 (p_w_r/6.116441)) −1) dewc_s = 240.7263/((7.591386)/(log10 (p_w_s/6.116441)) −1) dewf_r = dewc_r * 1.8 + 32 dewf_s = dewc_s * 1.8 + 32 altitude_Corr_Factor = Double(1 − 0.0000308 * (elev_ft)) let part1 = (return_C_Calibrated+(0.0065*Double(elev_ft)) + 273.15) p_tot = Float(1013.25 * pow (1−((0.0065 * Double(elev_ft)/part1)),5.257)) Let test1 = pow (return_RH_Calibrated,1.5]*atan (0.023101*return_RH_Calibrated)−4.686035; let change = atan (return_C_Calibrated+return_RH_Calibrated)−atan (return_RH_Calibrated− 1.676331)+0.00391838; t_wetc_r = Float(return_C_Calibrated * atan (0.151977*(pow ((return_RH_Calibrated+8.313659), (0.5)))))+Float(change)*Float(test1) let part2 = atan (0.151977*(pow ((supply_RH_Calibrated+8.313659),(0.5)))) let test2 = (pow (supply_RH_Calibrated,1.5))*atan (0.023101*supply_RH_Calibrated)−4.686035 let Change1 = atan (supply_RH_Calibrated−1.676331)+0.00391838 t_wetc_s = Float(supply_C_Calibrated*part2+atan (supply_C_Calibrated+supply_RH_Calibrated)− Change1*test2) let t_wetf_od_one = Double(temp_adjust)*atan (0.151977*(pow ((Double (cooling_Tower_ODRH_Adjust)+8.313659), (0.5))))+atan (Double(temp_adjust+cooling_Tower_ODRH_Adjust)) let t_wetf_od_two = atan (Double(cooling_Tower_ODRH_Adjust)−1.676331)+0.00391838*(pow (Double(cooling_Tower_ODRH_Adjust),1.5))*atan (0.023101*Double(cooling_Tower_ODRH_Adjust))− 4.686035 t_wetf_od = Float(t_wetf_od_one − t_wetf_od_two) t_wetf_r = t_wetc_r *1.8 + 32 t_wetf_s = t_wetc_s * 1.8 + 32 mix_Rat_r = 621.9907*(p_w_r/(p_tot-p_w_r))*0.001 mix_Rat_s = 621.9907*(p_w_s/(p_tot-p_w_s))*0.001 let part3 = Float(mix_Rat_r)*(0.444*Float(return_F_Calibrated)+1061) enthalpy_r = Double(0.24*Float(return_F_Calibrated)+(part3)) let part4 = Float(mix_Rat_s)*(0.444*Float(supply_F_Calibrated)+1061) enthalpy_s = Double(0.24*Float(supply_F_Calibrated)+(part4)) enth_Change_Cool = Float(max (enthalpy_r-enthalpy_s,0)) enth_Change_Heat = Float(max (enthalpy_s-enthalpy_r,0)) enthalpy_Change = max (enth_Change_Cool,enth_Change_Heat) enthalpy_ChangeK = (enthalpy_Change−7.686)*2.326 temp_Split = Float(max (supply_F_Calibrated−return_F_Calibrated,return_F_Calibrated− supply_F_Calibrated)) sHR = ((0.24*(supply_F_Calibrated−return_F_Calibrated))/(enthalpy_s−enthalpy_r)) space_SHR = return_F_Calibrated*0.24/enthalpy_r

Algorithms may calculate the efficiency of the unit by calculating the sensible heat ratio, refrigerant capacity, temperature split, and contrast these results with a preset effective cubic feet per minute (CFM) range. An effective CFM range may be defined between 325 to 420 CFM calculated. However, this may not necessarily be the actual airflow across the indoor coil but effectively what the CFM would have to be to get a certain enthalpy, sensible heat ratio and temperature split at a given calculated refrigerant capacity in British thermal units per hour (BTU/h). Probe app may determine additional efficiency issues, low/high air flow across evaporator coil, dirty filters, undersized ductwork, electric heat running, etc. These calculations and efficiencies may be sent to one or more user devices and displayed.

if !cfmAdjusted { if heating_running { if cfmMinAdjusted { min_CFM_adjust = min_CFM_adjust / heating_Tons cfmMinAdjusted = false } if cfmMaxAdjusted { max_CFM_adjust = max_CFM_adjust / heating_Tons cfmMaxAdjusted = false } } else { if cfmMinAdjusted { min_CFM_adjust = min_CFM_adjust / Float(tonnage) cfmMinAdjusted = false } if cfmMaxAdjusted { max_CFM_adjust = max_CFM_adjust / Float(tonnage) cfmMaxAdjusted = false } } cfmAdjusted = true } //Fanlaws Method check if fanlawsMethod { self.fanLawsMethod( ) } else { var tempTonnage: Float = 0.0 if heating_running { tempTonnage = heating_Tons } else { tempTonnage = Float(tonnage) } effmoncalcDelegate?.onCFMChanged(cfm: ((max_CFM_adjust + min_CFM_adjust) / 2 * tempTonnage)) cfmDelegate?.cfmChanged(cfm: ((max_CFM_adjust + min_CFM_adjust) /2 * tempTonnage)) DuctWorkCalculations.getInstance( ).Real_time_CFM = Double(((max_CFM_adjust + min_CFM_adjust) / 2 * tempTonnage)) } //Efficiency Rated Equations − Heat min_Heating_Capacity_perc = min_Heating_Capacity_adj + min_Heating_Capacity_base min_Heating_Capacity = (min_Heating_Capacity_perc/100)*max_Heat_Capacity //Blower calc's − Capacity changes but airflow is constant if [blower_pref_ans==0]{ let part1 = ((self.return_F_Calibrated-self.supply_F_Calibrated)*1.085); let temp1 = Float(eff_calc_BTU)*Float(actual_Tons)/Float(tonnage_adjust)*Float(sHR); cool_CFM_calc=max(min_CFM_adjust,min(max_CFM_adjust,Float(temp1)/Float(part1))) let part2 = ((Float(supply_F_Calibrated)− Float(return_F_Calibrated))*Float(altitude_Corr_Factor)*1.085); heat_CFM_calc=max(min_CFM_adjust,min(max_CFM_adjust,12000*pL_Heating_Tons/heating_Tons*(1/part2) )) let part3 = ((Float(return_F_Calibrated)−Float(supply_F_Calibrated))*1.085); aHU_Cold_Flowrate=max(min_CFM_adjust,min(max_CFM_adjust,12000*actual_Tons/Float(tonnage_adjust)*F loat(sHR)/part3)) let part4 = ((Float(supply_F_Calibrated)− Float(return_F_Calibrated))*Float(altitude_Corr_Factor)*1.085); aHU_Hot_Flowrate=max(min_CFM_adjust,min(max_CFM_adjust,12000*actual_Tons/Float(tonnage_adjust)*(1 /part4)))} else //− Capacity and airflow proportionally changes { cool_CFM_calc=max(min_CFM_adjust,min(max_CFM_adjust,eff_calc_BTU*Float(sHR)/((Float(return_F_Calibra ted)−Float(supply_F_Calibrated))*1.085))) let part1 = ((self.supply_F_Calibrated−self.return_F_Calibrated)*self.altitude_Corr_Factor*1.085); heat_CFM_calc=max(min_CFM_adjust,min(max_CFM_adjust,Float(12000*(1/part1)))) let part2 = ((self.return_F_Calibrated−self.supply_F_Calibrated)*1.085); aHU_Cold_Flowrate=max(min_CFM_adjust,min(max_CFM_adjust,Float(eff_calc_BTU)*Float(sHR)/Float(part2) )) let part3 = ((self.supply_F_Calibrated−self.return_F_Calibrated)*self.altitude_Corr_Factor*1.085); aHU_Hot_Flowrate=max(min_CFM_adjust,min(max_CFM_adjust,Float(12000*(1/part3))))} //Heating calcs //Eff_BTU_Heating=Heat_CFM_calc*Enth_Change_Heat*4.56 eff_Heat_CFM=Float(12000*(1/((supply_F_Calibrated− return_F_Calibrated)*altitude_Corr_Factor*1.085))) heat_High_CFM_EffCalc=min((1−((eff_Heat_CFM-heat_CFM_calc)/eff_Heat_CFM))*100,100) heat_Low_CFM_EffCalc=max(0,min(100,(1−((heat_CFM_calc-eff_Heat_CFM)/heat_CFM_calc))*100)) heating_Efficiency=max(0.001,min(heat_Low_CFM_EffCalc,heat_High_CFM_EffCalc)) perc_money_Lost_Heat=100−heating_Efficiency! max_LL_Variance_Heat_High_CFM_EffCalc=min((1−((eff_Heat_CFM- min_CFM_adjust)/eff_Heat_CFM))*100,100) max_LL_Variance_Heat_Low_CFM_EffCalc=max(0,min(100,(1−((min_CFM_adjust− eff_Heat_CFM)/min_CFM_adjust))*100)) max_LL_Variance_Heating_Efficiency=max(0.001,min(max_LL_Variance_Heat_Low_CFM_EffCalc,max_LL_Varia nce_Heat_High_CFM_EffCalc)) max_UL_Variance_Heat_High_CFM_EffCalc=min((1−((eff_Heat_CFM− max_CFM_adjust)/eff_Heat_CFM))*100,100) max_UL_Variance_Heat_Low_CFM_EffCalc=max(0,min(100,(1−((max_CFM_adjust− eff_Heat_CFM)/max_CFM_adjust))*100)) max_UL_Variance_Heating_Efficiency=max(0.001,min(max_UL_Variance_Heat_Low_CFM_EffCalc,max_UL_Vari ance_Heat_High_CFM_EffCalc)) max_VarianceHeat_max = max (max_UL_Variance_Heating_Efficiency,max_LL_Variance_Heating_Efficiency) max_VarianceHeat_min = min (max_UL_Variance_Heating_Efficiency,max_LL_Variance_Heating_Efficiency) min_LL_Variance_Heat_High_CFM_EffCalc=min((1−((eff_Heat_CFM− min_CFM_adjust(/eff_Heat_CFM))*100,100) min_LL_Variance_Heat_Low_CFM_EffCalc=max(0,min(100,(1−((min_CFM_adjust− eff_Heat_CFM(/min_CFM_adjust))*100)) min_LL_Variance_Heating_Efficiency=max(0.001,min(min_LL_Variance_Heat_Low_CFM_EffCalc,min_LL_Varia nce_Heat_High_CFM_EffCalc)) min_UL_Variance_Heat_High_CFM_EffCalc=min((1−((eff_Heat_CFM− max_CFM_adjust)/eff_Heat_CFM))*100,100) min_UL_Variance_Heat_Low_CFM_EffCalc=max(0,min(100,(1−((max_CFM_adjust− eff_Heat_CFM)/max_CFM_adjust))*100)) min_UL_Variance_Heating_Efficiency=max(0.001,min(min_UL_Variance_Heat_Low_CFM_EffCalc,min_UL_Varia nce_Heat_High_CFM_EffCalc)) min_VarianceHeat_max = max (min_LL_Variance_Heating_Efficiency,min_UL_Variance_Heating_Efficiency) min_VarianceHeat_min = min (min_LL_Variance_Heating_Efficiency,min_UL_Variance_Heating_Efficiency) variance_Heating_min = heating_Efficiency!−max (0, min (max_VarianceHeat_min,min_VarianceHeat_min)) Variance_Heating_max = max (heating_Efficiency!, max (max_VarianceHeat_max,min_VarianceHeat_max))−heating_Efficiency! //cooling calcs eff_BTU_Cooling=cool_CFM_calc*enth_Change_Cool*4.56 cool_High_CFM_EffCalc=min(100,(eff_BTU_Cooling/eff_calc_BTU)*100) cool_Low_CFM_EffCalc=max(0,min(100,(1−((eff_BTU_Cooling-eff_calc_BTU)/eff_calc_BTU))*100)) cooling_Efficiency=max(0.001,min(cool_High_CFM_EffCalc,cool_Low_CFM_EffCalc)) //Perc_Money_Lost_Cool=((eff_calc_BTU/((cooling_Efficiency/100)*eff_calc_BTU))−1)*100 perc_money_Lost_Cool=100-cooling_Efficiency! max_LL_Variance_Eff_BTU_Cooling=min_CFM_adjust*enth_Change_Cool*4.56 max_LL_Variance_Cool_High_CFM_EffCalc=min(100,(max_LL_Variance_Eff_BTU_Cooling/eff_calc_BTU)*100) max_LL_Variance_Cool_Low_CFM_EffCalc=max(0,min(100,(1−((max_LL_Variance_Eff_BTU_Cooling− eff_calc_BTU)/eff_calc_BTU))*100)) max_LL_Variance_cooling_Efficiency=max(0.001,min(max_LL_Variance_Cool_High_CFM_EffCalc,max_LL_Varia nce_Cool_Low_CFM_EffCalc)) max_UL_Variance_Eff_BTU_Cooling=max_CFM_adjust*enth_Change_Cool*4.56 max_UL_Variance_Cool_High_CFM_EffCalc=min(100,(max_UL_Variance_Eff_BTU_Cooling/eff_calc_BTU)*100) max_UL_Variance_Cool_Low_CFM_EffCalc=max(0,min(100,(1−((max_UL_Variance_Eff_BTU_Cooling− eff_calc_BTU)/eff_calc_BTU))*100)) max_UL_Variance_cooling_Efficiency=max(0.001,min(max_UL_Variance_Cool_High_CFM_EffCalc,max_UL_Vari ance_Cool_Low_CFM_EffCalc)) max_VarianceCool_max = max (max_UL_Variance_cooling_Efficiency,max_LL_Variance_cooling_Efficiency) max_VarianceCool_min = min (max_UL_Variance_cooling_Efficiency,max_LL_Variance_cooling_Efficiency) min_LL_Variance_Eff_BTU_Cooling=min_CFM_adjust*enth_Change_Cool*4.56 min_LL_Variance_Cool_High_CFM_EffCalc=min(100,(min_LL_Variance_Eff_BTU_Cooling/eff_calc_BTU)*100) min_LL_Variance_Cool_Low_CFM_EffCalc=max(0,min(100,(1−((min_LL_Variance_Eff_BTU_Cooling− eff_calc_BTU)/eff_calc_BTU))*100)) min_LL_Variance_cooling_Efficiency=max(0.001,min(min_LL_Variance_Cool_High_CFM_EffCalc,min_LL_Varian ce_Cool_Low_CFM_EffCalc)) min_UL_Variance_Eff_BTU_Cooling=max_CFM_adjust*enth_Change_Cool*4.56 min_UL_Variance_Cool_High_CFM_EffCalc=min(100,(min_UL_Variance_Eff_BTU_Cooling/eff_calc_BTU)*100) min_UL_Variance_Cool_Low_CFM_EffCalc=max(0,min(100,(1−((min_UL_Variance_Eff_BTU_Cooling− eff_calc_BTU)/eff_calc_BTU))*100)) min_UL_Variance_cooling_Efficiency=max(0.001,min(min_UL_Variance_Cool_High_CFM_EffCalc,min_UL_Varia nce_Cool_Low_CFM_EffCalc)) min_VarianceCool_max = max (min_LL_Variance_cooling_Efficiency,min_UL_Variance_cooling_Efficiency) min_VarianceCool_min = min (min_LL_Variance_cooling_Efficiency,min_UL_Variance_cooling_Efficiency) variance_Cooling_min = cooling_Efficiency!−max (0, min (max_VarianceCool_min,min_VarianceCool_min)) variance_Cooling_max = max (cooling_Efficiency!, max (max_VarianceCool_max,min_VarianceCool_max))− cooling_Efficiency!

Probe app 112 may be configured to also calculate variable speed compressor capacity in HVACR system 105 that include a compressor. To determine part load capacity or energy calculations such as SEER ratings, algorithms may take the rated load amps of the compressor and the actual amp draw to calculate compressor part load capacity. Probes 104 may include a one amp sensor per compressor and a one amp sensor to measure total amp draw to condenser fan motors and/or system components so that energy consumption can be computed. These calculations may be sent to one or more user devices and displayed.

compressor_max_ watts = compressor_kwatts_adjust*1000 pL_Compressor_max_ watts = pL_Compressor_kwatts_adjust*1000 max_Cool_Capacity = (Float(tonnage)*eff_calc_BTU) pL_Compr_max_Cool_Capacity = (pLcompressor_Tonnage_adjust*eff_calc_BTU) min_Cooling_Capacity_perc = min_Cooling_Capacity_adj + min_Cooling_Capacity_base min_Cooling_Capacity = (min_Cooling_Capacity_perc/100)*max_Cool_Capacity pLR_min_cap = ((min_Cooling_Capacity_perc−10.67)/0.8933) pLR_PLcompr_Capacity = ((0.8933*pLcompressor_Loadpercentage_adjust]+10.67)/100*pL_Compr_max_Cool_Capacity cool_Watts_multiplier = 1+((100-pLR_min_cap)/pLR_min_cap) pLComp_min_Cooling_Watts = min_Cooling_Capacity*0.293/coP pL_LowWatts = pLcompressor_Tonnage_adjust*eff_calc_BTU*0.293/coP fl_LowWatts = Float(tonnage)*eff_calc_BTU*0.293/coP //Partloaded Compressor Tonnage Equations if ((cool_Stage_Type==1) || (cool_Stage_Type==0)) {actual_Tons=Float(tonnage) system_Partload_Perc_realtime = 100 cool_CoP_realtime_calc = coP cool_CoP_full_calc = coP} else {actual_Tons=actual_Tons1+(pLR_PLcompr_Capacity/max_Cool_Capacity*pLcompressor_Tonnage_adjust) system_Partload_Perc_realtime = actual_Tons/Float(tonnage*100) if [actual_Tons1==0) { //PL Compressor Capacity Equations if [pL_Compressor_max_watts>pL_LowWatts) //PL must be more efficient than full capacity {pLCool_CoP_fullcap_calc_PLComp = pL_Compr_max_Cool_Capacity/(pLComp_min_Cooling_Watts*cool_Watts_multiplier) pLCool_COP_realtime_slope_calc = ((coP− pLCool_CoP_fullcap_calc_PLComp)/(pLcompressor_Loadpercentage_adjust−100)) pLCool_COP_realtime_yrint_calc = pLCool_CoP_fullcap_calc_PLComp− (pLCool_COP_realtime_slope_calc*100) cool_CoP_realtime_calc = (pLCool_COP_realtime_slope_calc*pLcompressor_Loadpercentage_adjust)+pLCool_COP_realtime_yrint_calc cool_CoP_full_calc = pLCool_CoP_fullcap_calc_PLComp } else //full load is more efficienct than part load {pLF = 1−(0.25*(1−(pLcompressor_Loadpercentage_adjust/100))) pLcool_CoP_realtime_calc = pLF * coP pLCool_CoP_fullcap_calc_PLComp = coP cool_CoP_realtime_calc = pLcool_CoP_realtime_calc cool_CoP_full_calc = pLCool_CoP_fullcap_calc_PLComp}} else { //Full system PL Capacity Equations if (compressor_max_ watts > fl_LowWatts) //PL must be more efficient than full capacity {min_Cooling_Watts = min_Cooling_Capacity*0.293/coP cool_CoP_full_calc = max_Cool_Capacity/(min_Cooling_Watts*cool_Watts-multiplier) cool_CoP_realtime_slope_calc = ((coP-cool_CoP_full_calc)/(system_Partload_Perc_realtime−100)) cool_CoP_realtime_yint_calc = cool_CoP_full_calc−(cool_CoP_realtime_slope_calc*100) cool_CoP_realtime_calc = (cool_CoP_realtime_slope_calc*system_Partload_Perc_realtime]+cool_CoP_realtime_yint_calc } else //full load is more efficienct than part load {pLF = 1−(0.25*(1−(system_Partload_Perc_realtime/100))) cool_CoP_realtime_calc = pLF * coP cool_CoP_full_calc = coP}}} if (heat_Alarm_Type==0) { heating_Type=0.0000001 perc_Heat_bound=100 heating_Tons=heating_Type/12000 pL_Heating_Type=heating_Type pL_Heating_Tons=heating_Tons] else if (heat_Alarm_Type==1){ if ((heat_Stage_Type_tunnel==1) || (heat_Stage_Type_tunnel==0)) {heating_Type=(3412.14163313*(self.kw))+furnace_BTU_100*(furn_Heating_Eff/100) heating_Tons=heating_Type/12000 pL_Heating_Type=heating_Type pL_Heating_Tons=heating_Tons} else {heating_Type=(3412.14163313*(self.kw]]+furnace_BTU_100*(furn_Heating_Eff/100) heating_Tons=heating_Type/12000 pL_Heating_Type=(3412.14163313*(kw]*(heatstrip_Loadpercentage_adjust/100))+furnace_BTU_100*(minF uelCapacity_Loadpercentage_adjust/maxFuelCapacity_Loadpercentage_adjust)*(furn_Heating_Eff/100) pL_Heating_Tons=pL_Heating_Type/12000} perc_Heat_bound=perc_Furn_Health_Bound} else if (heat_Alarm_Type==2) {if ((heat_Stage_Type_tunnel==1) || (heat_Stage_Type_tunnel==0)) {actual_Tons=Float(tonnage) system_Partload_Perc_realtime = 100 cool_CoP_realtime_calc = coP cool_CoP_full_calc = coP heating_Type=(3412.14163313*(kw))+(eff_calc_BTU*actual_Tons) heating_Tons=heating_Type/12000 pL_Heating_Type=heating_Type pL_Heating_Tons=heating_Tons } else {actual_Tons=actual_Tons1+(pLR_PLcompr_Capacity/max_Cool_Capacity*pLcompressor_Tonnage_adjust) system_Partload_Perc_realtime = actual_Tons/Float(tonnage*100) heating_Type=(3412.14163313*(kw))+(eff_calc_BTU*actual_Tons) heating_Tons=heating_Type/12000 pL_Heating_Type=(3412.14163313*(kw)*(heatstrip_Loadpercentage_adjust/100))+(eff_calc_BTU*actual_Ton s) pL_Heating_Tons=pL_Heating_Type/12000 perc_Heat_bound=perc_HP_Health_Bound if (actual_Tons1==0) { //PL Compressor Capacity Equations if (pL_Compressor_max_watts>pL_LowWatts) //pL must be more efficient than full capacity {pLHeat_CoP_fullcap_calc_PLComp = pL_Compr_max_Heat_Capacity/(pLComp_min_Heating_Watts*Heat_Watts_multiplier) pLHeat_COP_realtime_slope_calc = ((coP_Heat− pLHeat_CoP_fullcap_calc_PLComp)/(pLcompressor_Loadpercentage_adjust−100)) pLHeat_COP_realtime_yint_calc = pLHeat_CoP_fullcap_calc_PLComp− (pLHeat_COP_realtime_slope_calc*100) Heat_CoP_realtime_calc = (pLHeat_COP_realtime_slope_calc*pLcompressor_Loadpercentage_adjust]+pLHeat_COP_realtime_yint_calc Heat_CoP_full_calc = pLHeat_CoP_fullcap_calc_PLComp } else //full load is more efficienct than part load {pLF = 1−(0.25*(1−(pLcompressor_Loadpercentage_adjust/100))) pLHeat_CoP_realtime_calc = pLF * coP_Heat pLHeat_CoP_fullcap_calc_PLComp = coP_Heat Heat_CoP_realtime_calc = pLHeat_CoP_realtime_calc Heat_CoP_full_calc = pLHeat_CoP_fullcap_calc_PLComp}} else { //Full system PL Capacity Equations if (compressor_max_watts > fl_LowWatts) //PL must be more efficient than full capacity {min_Heating_Watts = min_Heating_Capacity*0.293/coP_Heat Heat_CoP_full_calc = max_Heat_Capacity/(min_Heating_Watts*Heat_Watts_multiplier) Heat_CoP_realtime_slope_calc = ((coP_Heat−Heat_CoP_full_calc)/(system_Partload_Perc_realtime- 100)) Heat_CoP_realtime_yint_calc = Heat_CoP_full_calc−(Heat_CoP_realtime_slope_calc*100) Heat_CoP_realtime_calc = (Heat_CoP_realtime_slope_calc*system_Partload_Perc_realtime)+Heat_CoP_realtime_yint_calc} else //full load is more efficienct than part load {pLF = 1−(0.25*(1−(system_Partload_Perc_realtime/100))) Heat_CoP_realtime_calc = pLF * coP_Heat Heat_CoP_full_calc = coP_Heat}}} }

Probe app 112 may also be configured to calculate an efficiency across an evaporator/condenser barrel and boiler. In this embodiment, fluid probes 104 b may be placed at the barrel and measure inlet and outlet fluid temperatures. Using these temperatures, probe app 112 may calculate an effective flow rate per ton. The efficiency of the barrel may then be calculated by the deviation of the effective flow rate and gallon per minute per ton. Probe app may determine additional efficiency issues, such as fluid scale build up on the refrigerant tubes, low/high water flow, etc. These calculations and efficiencies may be sent to one or more user devices and displayed.

//Fluid Pump Calculations if (fluidpump_pref_ans==0)//non variable speed fluid pump { let part1 = 8.01*Float(specificHeat)*Float(spec_Grav_New)*Float(tonnage_adjust); chiller_Flowrate=eff_calc_BTU*actual_Tons/(part1*(Float(return_F_Calibrated)−Float(supply_F_Calibrated))); let part2 = 8.01*Float(specificHeat)*Float(spec_Grav_New)*Float(tonnage_adjust); chiller_Flowrate_Cond=eff_calc_BTU*1.25*actual_Tons/(part2*(Float(return_F_Calibrated)− Float(supply_F_Calibrated))); let part3 = 8.01*Float(specificHeat)*Float(spec_Grav_New)*heating_Tons; boiler_Flowrate=12000*pL_Heating_Tons/(part3*(Float(supply_F_Calibrated)−Float(return_F_Calibrated))); let part4 = 8.01*Float(specificGravity)*Float(specificHeat); coolingTower_ActFlowrate=eff_calc_BTU*1.25/(part4*(Float(return_F_Calibrated)− Float(supply_F_Calibrated)));} else //variable speed fluid pump { let part1 = 8.01*specificHeat*spec_Grav_New chiller_Flowrate=eff_calc_BTU/(part1*(Float(return_F_Calibrated)−Float(supply_F_Calibrated))); let part2 = 8.01*specificHeat*spec_Grav_New chiller_Flowrate_Cond=Float(eff_calc_BTU)*1.25/(part2*(Float(return_F_Calibrated)− Float(supply_F_Calibrated))); boiler_Flowrate=12 000/(8.01*Float(specificHeat)*Float(spec_Grav_New)*(Float(supply_F_Calibrated)− Float(return_F_Calibrated))); let part3 = 8.01*Float(specificGravity)*Float(specificHeat); coolingTower_ActFlowrate=Float(eff_calc_BTU]*1.25/(part3*(Float(return_F_Calibrated)− Float(supply_F_Calibrated)));} //Chiller Evap Barrel Calc.'s chiller_High_CFM_EffCalc = min (100, abs (fluid_FlowRate_PerTon/chiller_Flowrate_Cond*100)); chiller_Low_CFM_EffCalc=max (0, min (100,chiller_Flowrate/fluid_FlowRate_PerTon*100)); chiller_Efficiency=min (chiller_Low_CFM_EffCalc,chiller_High_CFM_EffCalc); perc_money_Lost_Chiller=100-chiller_Efficiency!; //Chiller Cond Barrel Calc.'s chiller_High_CFM_EffCalc_Cond = min (100, abs (fluid_FlowRate_PerTon/chiller_Flowrate_Cond*100)); chiller_Low_CFM_EffCalc_Cond=max (0, min (100,chiller_Flowrate_Cond/fluid_FlowRate_PerTon*100)); chiller_Efficiency_Cond=min (chiller_Low_CFM_EffCalc_Cond,chiller_High_CFM_EffCalc_Cond); perc_money_Lost_Chiller_Cond=100-chiller_Efficiency_Cond!; //Chill-Hot Water AHU Calc.'s eff_BTU_ChillAHU=aHU_Cold_Flowrate*enth_Change_Cool*4.56; chillAHU_High_CFM_EffCalc=min(100,(eff_BTU_ChillAHU/12000)*100); chillAHU_Low_CFM_EffCalc=max(0,min(100,(1−((eff_BTU_ChillAHU−12000)/12000))*100)); chillAHU_Efficiency=max(0.001,min(chillAHU_High_CFM_EffCalc,chillAHU_Low_CFM_EffCalc)); perc_money_Lost_ChillAHU=100−chillAHU_Efficiency!; eff_BTU_BoilerAHU=aHU_Hot_Flowrate*enth_Change_Heat*4.56; boilerAHU_High_CFM_EffCalc=min(100,(eff_BTU_BoilerAHU/12000)*100); boilerAHU_Low_CFM_EffCalc=max(0,min(100,(1−((eff_BTU_BoilerAHU−12000)/12000))*100)); boilerAHU_Efficiency=min(boilerAHU_Low_CFM_EffCalc,boilerAHU_High_CFM_EffCalc); perc_money_Lost_BoilerAHU=100−boilerAHU_Efficiency!; //Boiler Calc.'s boiler_High_CFM_EffCalc = max (0, min (100, [fluid_FlowRate_PerTon/boiler_Flowrate*100))); boiler_Low_CFM_EffCalc=max (0, min (100,boiler_Flowrate/fluid_FlowRate_PerTon*100)); boiler_Efficiency=min (boiler_Low_CFM_EffCalc,boiler_High_CFM_EffCalc); perc_money_Lost_Boiler=100-boiler_Efficiency!;

Probe app 112 may also be configured to calculate an efficiency across a cooling tower. In this embodiment, fluid probes 104 b may measure an outdoor temperature, an outdoor relative humidity, an inlet fluid temperature and an outlet fluid temperature. Using these measurements along with the user inputted manufacturer designed range/approach temperatures and industry standard flow rate, probe app 112 may calculate an efficiency of the cooling tower. Probe app 112 may determine other efficiency issues, such as when it is too humid outdoors for the tower to function properly, scale build up, low/high water flow, etc. These calculations and efficiencies may be sent to one or more user devices and displayed.

calc_Approach=t_wetf_od+Float(cooling_Tower_Approach_Adjust); coolingTower_High_CFM_EffCalc = min (100, abs (fluid_FlowRate_PerTon/coolingTower_ActFlowrate*100)); coolingTower_Low_CFM_EffCalc=max (0, min (100,coolingTower_ActFlowrate/fluid_FlowRate_PerTon*100)); coolingTower_Efficiency=min (coolingTower_Low_CFM_EffCalc,coolingTower_High_CFM_EffCalc); perc_money_Lost_Tower=100-coolingTower_Efficiency!;

Probe app 112 may further be configured to calculate indoor air quality variables, such as CO2 and/or CO levels, particle count, temperature, and volatile organic compounds. In this embodiment, air probes 104 a may be placed in the return plenum of HVACR system 105. This air probe 104 a may measure carbon dioxide levels, carbon monoxide levels, particle allergen count, and volatile organic compounds. These measured levels may be sent to one or more user devices and displayed.

Probe app 112 may also be configured to calculate the effective insulation value (R value) of a space. In this embodiment, air probes 104 a may be placed in an air distribution system of HVACR system 105. Air probes 104 a may take measurements and transmit data to probe app 112, which calculates an enthalpy change in a space and the efficiency of HVACR system 105 over a period of time while HVACR system 105 is running. This calculation may create a known British thermal unit (BTU) value of heat either removed or added to the space. HVACR system 105 may then be shut down and enthalpy values of the space measured again over a period of time. Probe app 112 may then calculate two outputs, effective space R value (ESRV) and duct space R value (DSRV). ESRV, effective space R value, is the R value of the conditioned space or the space being tested that is calculated using various heat transfer principles, weather data and real-time HVACR data analyzed in Probe App 112. The ESRV value is not adjusted for duct/building air leakage. DSRV, duct effective space R value, is the ESRV value adjusted for duct leakage information calculated or inputted into App 112.

Probe app 112 may further be configured to calculate an amount of duct leakage. In this embodiment, probe app 112 may calculate BTUs entering a space while HVACR system 105 is running and the heat gain/loss of the space in BTUs while the system is off. Probe app 112 may factor in real-time weather data during the time frame. Using this data, duct leakage percentage can be back calculated by using the calculated relationship between ESRV and DSRV as described in the section prior.

Ovr_Leak_Perc=(ESRV−DSRV)/ESRV Calc_Leak_CFM=Double(Real_time_CFM)*Ovr_Leak_Perc

Probe app 112 can be used to calculate Internal loads in a space. These calculations are essentially an interpolation of ESRV and DSRV values over time. Internal loads are essential to accurate energy calculations and are a constantly changing variable. Internal load calculations replace the use of fixed industry standard internal load values, reducing inaccurate estimations. These calculations can enable the possibility of real-time internal load readings, improving technology surrounding building and HVAC efficiency.

// Calculate Max Inernal Loads for every effective space btuh if tempmode==1 { max_internal_loads = ((abs(cooling_infiltration_hours − esbtuh) / 2)) } else { max_internal_loads = ((abs(heating_infiltration_hours − esbtuh) / 2)) } // Precalc for esrv min & max for n in 0...detlta_t_array_offTime.count −1 { if tempmode==1 { let calcEsrvEnd = detlta_t_array_offTime [n] * surfaceArea esrv_min_array.append(((1 / (esbtuh + max_internal_loads)) * calcEsrvEnd)) //let esrvmax = (1 / (esbtuh − max_internal_loads)) * calcEsrvEnd let esrvmax = (1 / (esbtuh + 0.000001)) * calcEsrvEnd if esrvmax < −1000 || esrvmax > 1000 { esrv_max_array.append(0) } else { esrv_max_array.append(esrvmax) } } else { let calcEsrvEnd = detlta_t_array_offTime [n] * surfaceArea esrv_min_array.append(((1 / (esbtuh + max_internal_loads)) * calcEsrvEnd)) //let esrvmax = (1 / (esbtuh − max_internal_loads)) * calcEsrvEnd let esrvmax = (1 / (esbtuh + 0.000001)) * calcEsrvEnd if esrvmax < −1000 || esrvmax > 1000 { esrv_max_array.append(0) } else { esrv_max_array.append(esrvmax) } } } effective_space_btuh = abs (esbtuh) var esrv_min_adapted_array = [Double] ( ) for esrvmin in esrv_min_array { if esrvmin <= −1 || esrvmin >= 1 { esrv_min_adapted_array.append(esrvmin) } } var esrv_max_adapted_array = [Double] ( ) for esrvmax in esrv_max_array { if esrvmax <= −1 || esrvmax >= 1 { esrv_max_adapted_array.append(esrvmax) } } let esrv_min = abs(esrv_min_adapted_array.average) let esrv_max = abs(esrv_max_adapted_array.average) let esrv_slope = abs(((esrv_min − esrv_max) / max_internal_loads)) //let esrv_slope = abs(((esrv_min − esrv_max) / moneySaver.internal_load_during_test)) var dsrv_min = 0.0 if tempmode==1 { dsrv_min = abs((1 / (cooling_infiltration_hours)) * detlta_t_array_offTime.average * surfaceArea) } else { dsrv_min = abs((1 / heating_infiltration_hours) * detlta_t_array_offTime.average * surfaceArea) } internal_loads = abs((esrv_max − dsrv_min) / (2 * esrv_slope))

Finally, probe app 112 may be configured to calculate when an air filter needs to be replaced. In this embodiment, probe app 112 first calculates enthalpy change, system efficiency and static pressure change over time across an indoor coil. The device could zero out when the system is running with a reduced static pressure and a calculation of the sensible heat ratio to monitor if the system was running dehumidification and/or humidification. As the air filter gets dirty, the sensible heat ratio will slightly decrease due to reduced airflow across the coil and the static pressure will follow an exponential trend upwards. At a certain value point, the device algorithms can trigger a message indicating that it is time to replace the air filters. In addition, the air filter life percentage could constantly be displayed with the monitoring type device.

Processor 114 or similar can transmit and receive data via hardwired, cellular, Wi-fi and/or Bluetooth to a thermostat controller. This data can be displayed or used in calculations inside of the thermostat controller. The data is not limited to the use of the controller but may also be relayed from the controller to an external server for further use such as delivering energy data to approved parties such as energy companies.

App 112 may be interfaced with zoning controls that control the air flow distribution system. The app could identify duct leakage, zone damper failures and aid in energy management solutions.

App 112 could be applied in the automotive industry by sending real-time HVAC diagnostic information to the vehicles server unit. This data could aid in vehicle diagnostics, vehicle air quality diagnostics and vehicle energy management.

FIG. 2 illustrates an exemplary air probe tool 202 and an exemplary fluid probe tool 210. Air probe tool 202 includes internal portion 204 and an external portion 206. When installed in an HVACR system, internal portion 204 may be configured to extend into an interior space within the HVACR system. To install air probe tool 202, a hole may be cut in a portion of an HVACR system and such that internal portion 204 of air probe tool 202 extends into an interior portion of the HVACR system. External portion 206 of air probe tool 202 may be positioned on the outside of the HVACR system.

In one embodiment, air probe tool 202 may be installed in an HVAC plenum 208. Plenum 208 may be a return plenum that is positioned between a first piece of HVACR evaporator coil and the first return duct inlet from the equipment. Alternatively, plenum 208 may be a supply plenum that is positioned between the evaporator coil and the first supply duct from the equipment. In one embodiment, two air probe tool 202 may be installed in an HVACR system. A first air probe tool may be installed in a supply plenum and a second air probe tool may be installed in a return plenum.

Air probe tool 202 may include one or more sensors in the either the internal portion 204, or the external portion 206, or both. For example, sensors within internal portion 204 of air probe tool 202 may measure a temperature, relative humidity, barometric pressure, elevation, air quality, and/or air flow rate within plenum 208. Sensors within external portion 206 of air probe tool 202 may measure a temperature, relative humidity, barometric pressure, elevation, air quality, and/or air flow rate outside of plenum 208.

Air probe tool 202 may include a transmission mechanism. For example, air probe tool 202 may include a wifi transmitter that is configured to transmit data through a network, such as the Internet, to an external webserver. In other embodiments, air probe tool 202 may include a Bluetooth transmitter that may transmit data to a user device. Alternatively still, air probe tool 202 may transmit data through a wired connection to a user device. Air probe tool 202 may include one or more internal batteries. Alternatively, air probe tool 202 may include a power cord that may be plugged into an outlet.

Fluid probe tool 210 may include an electronic portion 212 and a band portion 214. When installed in an HVACR system, band portion 214 of fluid probe tool 210 may be secured around a pipe or conduit that contains a liquid. Band portion 214 may be adjustable in size to ensure that electronic portion 212 is in contact with an exterior surface of the pipe or conduit that contains a liquid.

Electronic portion 212 may include one or more sensors that are configured to contact an exterior surface of a pipe or conduit to which fluid probe tool 210 is attached. In one embodiment, these sensors may include a thermometer that is configured to measure the temperature of a liquid inside of a pipe or conduit. Electronic portion 212 may include a thermometer that measures an air temperature outside of a pipe or conduit.

In one embodiment, one or more fluid probe tools 210 may be installed near inlets/outlets of evaporator barrels, condenser barrels, hot water boilers, and/or cooling towers. For example, in one embodiment, a first fluid probe tool may be installed near the external inlet side of an evaporator barrel and a second fluid probe may be installed near the external outlet side of the evaporator barrel. For reliable heat transfer readings, a good point of contact between the fluid probe and a pipe is necessary. In some cases, external piping insulation will need to be removed for good contact.

Fluid probe tool 210 may include a transmission mechanism. For example, fluid probe tool 210 may include a WiFi transmitter that is configured to transmit data through a network, such as the Internet, to an external web server. In other embodiments, fluid probe tool 210 may include a Bluetooth transmitter that may transmit data to a user device. Alternatively still, fluid probe tool 210 may transmit data through a wired connection to a user device. Fluid probe tool 210 may include one or more internal batteries. Alternatively, fluid probe tool 210 may include a power cord that may be plugged into an outlet.

FIG. 3 illustrates an exemplary monitoring system 300. Monitoring system 300 includes a pair of air probes 302, a fluid probe 304, and a controller 306. Air probes 302 include cables extending from their ends. These cables may be power cables that are configured to plug into an outlet. Alternatively, these cables may communicate measured data to an external device. In some embodiments, air probes 302 and fluid probe 304 may include an internal communication mechanism. These communication mechanism may allow the probes to communicate wirelessly via, for example, Bluetooth and/or WiFi. In some embodiments, probes 302, 304 may be connected via a wire to controller 306. Controller 306 may include an internal communication mechanism that allows for wireless communication to external devices. Fluid probe 306 includes a band and a pair of contact prongs that are configured to make contact with a pipe around which fluid probe 306 is secured.

FIG. 4 illustrates an exemplary login page from a probe app according to the present disclosure. The page illustrated in FIG. 4 may be a login screen. A user that has already created an account on the probe app may simply enter his or her email address and a password.

FIG. 5 illustrates an exemplary accounts page from a probe app. Once a user has logged into the probe app, he or she can choose to go into an existing customer account or create a new account. The new account may be a residential account or a commercial account.

FIG. 6 illustrates an exemplary reports page from a probe app. Through this page, a user can edit data and/or browse existing reports.

FIG. 7 illustrates an exemplary residential accounts page from a probe app. Through this page, a user can add a residential account.

FIG. 8 illustrates an exemplary commercial accounts page from a probe app. Through this page, a user can add a commercial account.

FIG. 9 illustrates an exemplary equipment type page from a probe app. Through this page, a user can select the type of device to which the user may connect. For example, the user may select either air probes or fluid probes.

FIG. 10 illustrates an exemplary input page from a probe app. Through this page, a user can provide input on known data so that an analysis can be conducted. The data is separated into five different categories: general, equipment, cooling/heating, duct work, and controls/financial.

FIG. 11 illustrates a exemplary general input page from a probe app. Through this page, a user can add general data. Specifically, a user may enter his or her zip code. Some of this data may automatically populate based on the zip code entered, such as air temperature and humidity. All historical data may be saved and used during analysis. Design Min Temp and Design Max Temp may be a calculation created from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) recommendation of a system designed to work consistently on historic averages.

FIG. 12 illustrates an exemplary equipment page from a probe app. Through this page, a user can add equipment data. Specifically, the user may select equipment type, tonnage and enter known airflow cubic feet per minute (CFM) across the indoor coil. If the airflow data is not known, a range may be used and a statistical output will be present in the analysis to show system efficiency accuracy.

FIG. 13 illustrates an exemplary cooling page from a probe app. Through this page, a user can add cooling data. Specifically, a user may enter known information that can easily be found on equipment nameplates or by looking up system model numbers. If the cooling system efficiency is already known, or if an analysis is to be performed without the system running, a user may select the cooling override option.

FIGS. 14 and 15 illustrate exemplary heating pages from a probe app. Through these pages, a user can add heating data.

FIG. 16 illustrates an exemplary controls/financial page from a probe app. Through this page, a user can add controls/financial data. For example, a user may enter a current utility rate and select the Air-Conditioning, Heating, and Refrigeration Institute (AHRI) efficiency of the system.

FIG. 17 illustrates an exemplary load test page from a probe app. Through this page, if a user needs to find a space R value, the user can run a load test. The load test outputs are necessary to receive accurate financial data. A user may select the Load Test button and follow instruction provided. The test may take approximately 45 minutes to complete. If the values are already known (for example if the test has already been run), the user may select test override and insert the known values.

FIGS. 18 and 19 illustrate exemplary load test advanced settings pages from a probe app. These pages may be advanced settings pages where a user can input device data calibrations. These calibrations may be saved in the probe app so that there is no need to calibrate every time the user logs onto the probe app.

FIGS. 20 to 25 illustrate exemplary technical analysis pages from a probe app. These pages provides real-time efficiencies for cooling and heating. These pages also display real-time space and system sensible heat ratios, recommendations on air speed settings and what seasonal energy efficiency ratio (SEER) the system is actually running. A user may select a code button to see receive potential reasons that the system is not running as efficiently as it could be running. These pages may also provide information on how leaky the ductwork is on the system. These pages may display the annual cooling and heating kwh from the HVACR system as well as how over or under sized the system is under the current conditions. Finally, these pages may display the minimum and maximum temperatures possible from the calculations.

FIGS. 26 to 29 illustrate exemplary financial analysis pages from a probe app. These pages display how much the cooling, heating, and ductwork is currently costing and an estimation of how much it will cost in the future. These pages also show how much money can be saved if the HVACR system was running at 100% efficiency or the space R-value was improved. The improved R-value may be modified through inputs, however the default may be current plus five. These pages may also show how much money the system currently costs based on the programming set in the input selection. These setpoints may be modified to see how bills would be impacted. These pages also provide a graphical view of the carbon footprint of the HVACR system and the outdoor temperature limits. The carbon footprint values may be pulled from a geographical database that analyzes the type of electricity produced to the address where the HVACR system is located. The carbon footprint may also be calculated based on total fuel consumption, if applicable.

FIGS. 30 to 34 illustrate exemplary reporting pages from a probe app. These pages allow a user to select the type of report to view. Technical reports may include very specific details of the system. A customer report may be more simple to understand. FIGS. 32 to 34 illustrate an exemplary customer report. Once the type of report is selected, the report(s) may be sent to an email address provided or a pdf view may be generated and saved or printed out.

FIG. 35 illustrates an example computer system 400 that may be employed in a system for measuring and displaying HVACR efficiencies. In some embodiments, the computer system 400 may be part of any of the systems or devices described in this disclosure. For example, the computer system 400 may be part of any of the probes 104, webserver 106, user device 108 and mobile application marketplace server 110 of FIG. 1.

The computer system 400 may include a processor 402, a memory 404, a file system 406, a communication unit 408, an operating system 410, a user interface 412, and an application 414, which all may be communicatively coupled. In some embodiments, the computer system may be, for example, a desktop computer, a client computer, a server computer, a mobile phone, a laptop computer, a smartphone, a smartwatch, a tablet computer, a portable music player, or any other computer system.

Generally, the processor 402 may include any suitable special-purpose or general-purpose computer, computing entity, or processing device including various computer hardware or software applications and may be configured to execute instructions stored on any applicable computer-readable storage media. For example, the processor 402 may include a microprocessor, a microcontroller, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a Field-Programmable Gate Array (FPGA), or any other digital or analog circuitry configured to interpret and/or to execute program instructions and/or to process data, or any combination thereof. In some embodiments, the processor 402 may interpret and/or execute program instructions and/or process data stored in the memory 404 and/or the file system 406. In some embodiments, the processor 402 may fetch program instructions from the file system 406 and load the program instructions into the memory 404. After the program instructions are loaded into the memory 404, the processor 402 may execute the program instructions.

The memory 404 and the file system 406 may include computer-readable storage media for carrying or having stored thereon computer-executable instructions or data structures. Such computer-readable storage media may be any available non-transitory media that may be accessed by a general-purpose or special-purpose computer, such as the processor 402. By way of example, and not limitation, such computer-readable storage media may include non-transitory computer-readable storage media including Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory devices (e.g., solid state memory devices), or any other storage media which may be used to carry or store desired program code in the form of computer-executable instructions or data structures and which may be accessed by a general-purpose or special-purpose computer. Combinations of the above may also be included within the scope of computer-readable storage media. Computer-executable instructions may include, for example, instructions and data configured to cause the processor 402 to perform a certain operation or group of operations. These computer-executable instructions may be included, for example, in the operating system 410, in one or more applications, such as probes 104, webserver 106, probe app 112, processor 114, or user device 108 of FIG. 1, or in some combination thereof.

The communication unit 408 may include any component, device, system, or combination thereof configured to transmit or receive information over a network, such as the network 102 of FIG. 1. In some embodiments, the communication unit 408 may communicate with other devices at other locations, the same location, or even other components within the same system. For example, the communication unit 408 may include a modem, a network card (wireless or wired), an infrared communication device, a wireless communication device (such as an antenna), and/or chipset (such as a Bluetooth device, an 802.6 device (e.g., Metropolitan Area Network (MAN)), a WiFi device, a WiMax device, a cellular communication device, etc.), and/or the like. The communication unit 408 may permit data to be exchanged with a network and/or any other devices or systems, such as those described in the present disclosure.

The operating system 410 may be configured to manage hardware and software resources of the computer system 400 and configured to provide common services for the computer system 400.

The user interface 412 may include any device configured to allow a user to interface with the computer system 400. For example, the user interface 412 may include a display, such as an LCD, LED, or other display, that is configured to present video, text, application user interfaces, and other data as directed by the processor 402. The user interface 412 may further include a mouse, a track pad, a keyboard, a touchscreen, volume controls, other buttons, a speaker, a microphone, a camera, any peripheral device, or other input or output device. The user interface 412 may receive input from a user and provide the input to the processor 402. Similarly, the user interface 412 may present output to a user.

The application 414 may be one or more computer-readable instructions stored on one or more non-transitory computer-readable media, such as the memory 404 or the file system 406, that, when executed by the processor 402, is configured to perform one or more actions of the system. In some embodiments, the application 414 (e.g., app) may be part of the operating system 410 or may be part of an application of the computer system 400, or may be some combination thereof.

Modifications, additions, or omissions may be made to the computer system 400 without departing from the scope of the present disclosure. For example, although each is illustrated as a single component in FIG. 35, any of the components 402-414 of the computer system 400 may include multiple similar components that function collectively and are communicatively coupled. Further, although illustrated as a single computer system, it is understood that the computer system 400 may include multiple physical or virtual computer systems that are networked together, such as in a cloud computing environment, a multitenancy environment, or a virtualization environment.

As indicated above, the embodiments described herein may include the use of a special purpose or general-purpose computer (e.g., the processor 402 of FIG. 35) including various computer hardware or software applications, as discussed in greater detail below. Further, as indicated above, embodiments described herein may be implemented using computer-readable media (e.g., the memory 404 or file system 406 of FIG. 35) for carrying or having computer-executable instructions or data structures stored thereon.

In some embodiments, the different components and applications described herein may be implemented as objects or processes that execute on a computer system (e.g., as separate threads). While some of the methods described herein are generally described as being implemented in software (stored on and/or executed by general purpose hardware), specific hardware implementations or a combination of software and specific hardware implementations are also possible and contemplated.

In accordance with common practice, the various features illustrated in the drawings may not be drawn to scale. The illustrations presented in the present disclosure are not meant to be actual views of any particular apparatus (e.g., device, system, etc.) or method, but are merely example representations that are employed to describe various embodiments of the disclosure. Accordingly, the dimensions of the various features may be arbitrarily expanded or reduced for clarity. In addition, some of the drawings may be simplified for clarity. Thus, the drawings may not depict all of the components of a given apparatus (e.g., device) or all operations of a particular method.

Terms used herein and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including, but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes, but is not limited to,” etc.).

Additionally, if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitation is explicitly recited, it is understood that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” or “one or more of A, B, and C, etc.” is used, in general such a construction is intended to include A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B, and C together, etc. For example, the use of the term “and/or” is intended to be construed in this manner.

Further, any disjunctive word or phrase presenting two or more alternative terms, whether in the summary, detailed description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” should be understood to include the possibilities of “A” or “B” or “A and B.”

Additionally, the use of the terms “first,” “second,” “third,” etc., are not necessarily used herein to connote a specific order or number of elements. Generally, the terms “first,” “second,” “third,” etc., are used to distinguish between different elements as generic identifiers. Absence a showing that the terms “first,” “second,” “third,” etc., connote a specific order, these terms should not be understood to connote a specific order. Furthermore, absence a showing that the terms first,” “second,” “third,” etc., connote a specific number of elements, these terms should not be understood to connote a specific number of elements. For example, a first widget may be described as having a first side and a second widget may be described as having a second side. The use of the term “second side” with respect to the second widget may be to distinguish such side of the second widget from the “first side” of the first widget and not to connote that the second widget has two sides.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention as claimed to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described to explain practical applications, to thereby enable others skilled in the art to utilize the invention as claimed and various embodiments with various modifications as may be suited to the particular use contemplated. 

1. A computer-implemented method for measuring the efficiency of an HVACR system, the computer-implemented method comprising: receiving data regarding HVACR system at a processor, wherein the data is received from one or more probes installed on the HVACR system; processing the data to determine an efficiency of the HVACR system; and displaying the efficiency data on a network device.
 2. The method of claim 1, wherein the probe is an air probe and the received data includes at least one of a temperature, a relative humidity, a barometric pressure, and an air flow rate.
 3. The method of claim 1, wherein the probe is a fluid probe and the received data includes a temperature. 